Eliciting the child's voice in adverse event reporting in oncology trials: Cognitive interview findings from the Pediatric Patient‐Reported Outcomes version of the Common Terminology Criteria for Adverse Events initiative
Bibliographic record
Abstract
BACKGROUND: Adverse event (AE) reporting in oncology trials is required, but current practice does not directly integrate the child's voice. The Pediatric Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) is being developed to assess symptomatic AEs via child/adolescent self-report or proxy-report. This qualitative study evaluates the child's/adolescent's understanding and ability to provide valid responses to the PRO-CTCAE to inform questionnaire refinements and confirm content validity. PROCEDURE: From seven pediatric research hospitals, children/adolescents ages 7-15 years who were diagnosed with cancer and receiving treatment were eligible, along with their parent-proxies. The Pediatric PRO-CTCAE includes 130 questions that assess 62 symptomatic AEs capturing symptom frequency, severity, interference, or presence. Cognitive interviews with retrospective probing were completed with children in the age groups of 7-8, 9-12, and 13-15 years. The children/adolescents and proxies were interviewed independently. RESULTS: Two rounds of interviews involved 81 children and adolescents and 74 parent-proxies. Fifteen of the 62 AE terms were revised after Round 1, including refinements to the questions assessing symptom severity. Most participants rated the PRO-CTCAE AE items as "very easy" or "somewhat easy" and were able to read, understand, and provide valid responses to questions. A few AE items assessing rare events were challenging to understand. CONCLUSIONS: The Pediatric and Proxy PRO-CTCAE performed well among children and adolescents and their proxies, supporting its content validity. Data from PRO-CTCAE may improve symptomatic AE reporting in clinical trials and enhance the quality of care that children receive.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".